Debiasing Standard Siren Inference of the Hubble Constant with Marginal Neural Ratio Estimation

Abstract

Gravitational wave (GW) standard sirens may resolve the Hubble tension, provided that standard siren inference of $H_0$ is free from systematic biases. However, standard sirens from binary neutron star (BNS) mergers suffer from two sources of systematic bias, one arising from the anisotropy of GW emission, and the other from the anisotropy of electromagnetic (EM) emission from the kilonova. For an observed sample of BNS mergers, the traditional Bayesian approach to debiasing involves the direct computation of the detection likelihood. This is infeasible for large samples of detected BNS merger due to the high dimensionality of the parameter space governing merger detection. In this study, we bypass this computation by fitting the Hubble constant to forward simulations of the observed GW and EM data under a simulation-based inference (SBI) framework using marginal neural ratio estimation. A key innovation of our method is the inclusion of BNS mergers which were only detected in GW, which allows for estimation of the bias introduced by EM anisotropy. Our method corrects for $\sim$90$\%$ of the bias in the inferred value of $H_0$ when telescope follow-up observations of BNS mergers have extensive tiling of the merger localization region, using known telescope sensitivities and assuming a model of kilonova emission. Our SBI-based method thus enables a debiased inference of the Hubble constant of BNS mergers, including both mergers with detected EM counterparts and those without.

Accepted MNRAS, 14 pages, 10 figures

References in corpus (8)

• GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
• Gravitational Waves and Gamma-rays from a Binary Neutron Star Merger: GW170817 and GRB 170817A
• GW190814: Gravitational Waves from the Coalescence of a 23 M${}_{\odot }$ Black Hole with a 2.6 M${}_{\odot }$ Compact Object
• A gravitational-wave standard siren measurement of the Hubble constant
• Robust parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library
• The Electromagnetic Counterpart of the Binary Neutron Star Merger LIGO/Virgo GW170817. I. Dark Energy Camera Discovery of the Optical Counterpart
• Kilonovae
• Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning

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